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Found 7 papers, 4 papers with code
Probabilistically Robust Watermarking of Neural Networks
As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model.
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing
Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude.
Translate your gibberish: black-box adversarial attack on machine translation systems
Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine translation systems.
Smoothed Embeddings for Certified Few-Shot Learning
In this work, we extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings.
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions.
On adversarial patches: real-world attack on ArcFace-100 face recognition system
We examine security of one of the best public face recognition systems, LResNet100E-IR with ArcFace loss, and propose a simple method to attack it in the physical world.
Real-world adversarial attack on MTCNN face detection system
Recent studies proved that deep learning approaches achieve remarkable results on face detection task.
